Rapid Prediction of Seismic Incident Angle's Influence on the Damage Level of RC Buildings Using Artificial Neural Networks

The angle of seismic excitation is a significant factor in the seismic response of RC buildings. The procedure required for the calculation of the angle for which the potential seismic damage is maximized (critical angle) contains multiple nonlinear time history analyses, each using different angles of incidence. Moreover, the seismic codes recommend the application of more than one accelerogram for the evaluation of seismic response; thus, the whole procedure becomes time consuming. Herein, a method to reduce the time required for the estimation of the critical angle based on multilayered feedforward perceptron neural networks is proposed. The basic idea is the detection of cases in which the critical angle increases the class of seismic damage compared to the class that arises from the application of the seismic motion along the buildings' structural axes. To this end, the problem is expressed and solved as a pattern recognition problem. The ratios of seismic parameters' values along the two horizontal seismic records' components, as well as appropriately chosen structural parameters, were used as the inputs of the networks. The results of analyses show that the neural networks can reliably detect the cases in which the calculation of the critical angle is essential.